Adding two small changes.

inference/bayes.qmd

@@ -114,7 +114,7 @@ $$
 \frac{0.99 \cdot 0.00025}{0.99 \cdot 0.00025 + 0.01 \cdot (.99975)}  =  0.02 
 $$
 
-According to the above, despite the test having 0.99 accuracy, the probability of having the disease given a positive test is only 0.02. This might seem counter-intuitive to some, but it ss because we must factor in the very rare probability that a randomly chosen person has the disease. To illustrate this, we run a Monte Carlo simulation.
+According to the above, despite the test having 0.99 accuracy, the probability of having the disease given a positive test is only 0.02. This might seem counter-intuitive to some, but it is because we must factor in the very rare probability that a randomly chosen person has the disease. To illustrate this, we run a Monte Carlo simulation.
 
 ### Bayes theorem simulation
 

prob/discrete-probability.qmd

@@ -16,7 +16,7 @@ A more tangible way to think about the probability of an event is as the proport
 
 We use the notation $\mbox{Pr}(A)$ to denote the probability of event $A$ occurring. We use the very general term *event* to refer to things that can happen when something occurs by chance. In our previous example, the event was "picking a red bead." In a political poll, where we randomly phone 100 likely voters at random, an example of an event is "calling 48 Democrats and 52 Republicans."
 
-In data science applications, we often encounter continuous variables. These events will often be questions, such as "Is this person taller than 6 feet?" In these cases, we represent events in a more mathematical form: $X \geq 6$. We will see more of these examples later, but for now, we will focus on categorical data.
+In data science applications, we often encounter continuous variables. These events will often be questions, such as "Is this person taller than 6 feet?" In these cases, we represent events in a more mathematical form: $X > 6$. We will see more of these examples later, but for now, we will focus on categorical data.
 
 ## Probability distributions